Kingsnorth AP(1)(2), Whelan ME(3), Orme MW(4)(5), Routen AC(6), Sherar LB(1)(2)(7), Esliger DW(1)(2)(7). Author information:
(1)School of Sport, Exercise and Health Sciences, Loughborough University,
Leicestershire, LE11 3TU, UK.
(2)National Centre for Sport and Exercise Medicine, Loughborough University,
(3)Centre for Intelligent Healthcare, Faculty of Health and Life Sciences,
Coventry University, CV1 5FB, UK.
(4)Department of Respiratory Sciences, University of Leicester, Leicestershire,
(5)Centre for Exercise and Rehabilitation Science, NIHR Leicester Biomedical
Research Centre-Respiratory, Leicestershire, LE3 9QP, UK.
(6)NIHR Applied Research Collaboration East Midlands (ARC EM), Diabetes Research
Centre, University of Leicester, LE5 4PW, UK.
(7)NIHR Leicester Biomedical Research Centre-Lifestyle, Leicestershire, LE5 4PW,
Like many wearables, flash glucose monitoring relies on user compliance and is subject to missing data. As recent research is beginning to utilise glucose technologies as behaviour change tools, it is important to understand whether missing data are tolerable. Complete Freestyle Libre data files were amputed to remove 1-6 h of data both at random and over mealtimes (breakfast, lunch, and dinner). Absolute percent errors (MAPE) and intraclass correlation coefficients (ICC) were calculated to evaluate agreement and reliability. Thirty-two (91%) participants provided at least 1 complete day (24 h) of data (age: 44.8 ± 8.6 years, female: 18 (56%); mean fasting glucose: 5.0 ± 0.6 mmol/L). Mean and continuous overall net glycaemic action (CONGA) (60 min) were robust to data loss (MAPE ≤3%). Larger errors were calculated for standard deviation, coefficient of variation (CV) and mean amplitude of glycaemic excursions (MAGE) at increasing missingness (MAPE: 2%-10%, 2%-9%, and 4%-18%, respectively). ICC decreased as missing data increased, with most indicating excellent reliability (>0.9) apart from certain MAGE ICCs, which indicated good reliability (0.84-0.9). Researchers and clinicians should be aware of the potential for larger errors when reporting standard deviation, CV, and MAGE at higher rates of data loss in nondiabetic populations. But where mean and CONGA are of interest, data loss is less of a concern. Novelty: As research now utilises flash glucose monitoring as behavioural change tools in nondiabetic populations, it is important to consider the influence of missing data. Glycaemic variability indices of mean and CONGA are robust to data loss, but standard deviation, CV, and MAGE are influenced at higher rates of missingness.
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